Performing radio frequency matching control using a model-based digital twin
A method includes causing manufacturing equipment to generate a RF signal to energize a processing chamber associated with the manufacturing equipment. The method further includes receiving, from one or more sensors associated with the manufacturing equipment, current trace data associated with the RF signal. The method further includes updating impedance values of a digital replica associated with the manufacturing equipment based on the current trace data. The method further includes obtaining, from the digital replica, one or more outputs indicative of predictive data. The method further includes causing, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment.
1. A method comprising:
causing manufacturing equipment to generate a RF signal to energize a processing chamber associated with the manufacturing equipment;
receiving, from one or more sensors associated with the manufacturing equipment, current trace data associated the RF signal;
updating impedance values of a digital replica associated with the manufacturing equipment based on the current trace data, wherein the digital replica comprises a model reflecting a virtual representation of physical elements and dynamics of how the manufacturing equipment operates;
obtaining, from the digital replica, one or more outputs indicative of predictive data; and
causing, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment.
2. The method of claim 1 , further comprising:
responsive to determining that digital replica fails to satisfy an accuracy threshold criterion based on the current trace data, performing an optimization of the digital replica.
3. The method of claim 1 , wherein updating the impedance values of the digital replica comprises:
updating a processing chamber model associated with the digital replica using trace data from an output impedance sensor, wherein the output impedance sensor is associated with a matching network of the manufacturing equipment.
4. The method of claim 1 , wherein updating the impedance values of the digital replica comprises:
updating a matching network model associated with the digital replica using trace data from an input impedance sensor and an output impedance sensor, wherein the input impedance sensor and the output impedance sensor are associated with a matching network of the manufacturing equipment.
5. The method of claim 1 , wherein the one or more outputs indicative of predictive data are generated using a trained machine learning model.
6. The method of claim 1 , wherein the predictive data comprises one or more tune settings for one or more variable capacitors associated with a matching network of the manufacturing equipment.
7. The method of claim 1 , wherein the corrective action comprises adjusting one or more variable capacitors associated with a matching network of the manufacturing equipment based on the predictive data.
8. A system comprising:
a memory; and
a processing device, coupled to the memory, to:
cause manufacturing equipment to generate a RF signal to energize a processing chamber associated with the manufacturing equipment;
receive, from one or more sensors associated with the manufacturing equipment, current trace data associated the RF signal;
update impedance values of a digital replica associated with the manufacturing equipment based on the current trace data, wherein the digital replica comprises a model reflecting a virtual representation of physical elements and dynamics of how the manufacturing equipment operates;
obtain, from the digital replica, one or more outputs indicative of predictive data; and
cause, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment.
9. The system of claim 8 , wherein the processing device is further to:
responsive to determining that digital replica fails to satisfy an accuracy threshold criterion based on the current trace data, perform an optimization of the digital replica.
10. The system of claim 8 , wherein to update the impedance values of the digital replica, the processing device is further to:
update a processing chamber model associated with the digital replica using trace data from an output impedance sensor, wherein the output impedance sensor is associated with a matching network of the manufacturing equipment.
11. The system of claim 8 , wherein to update the impedance values of the digital replica, the processing device is further to:
update a matching network model associated with the digital replica using trace data from an input impedance sensor and an output impedance sensor, wherein the input impedance sensor and the output impedance sensor are associated with a matching network of the manufacturing equipment.
12. The system of claim 8 , wherein the one or more outputs indicative of predictive data are generated using a trained machine learning model.
13. The system of claim 8 , wherein the predictive data comprises one or more tune settings for one or more variable capacitors associated with a matching network of the manufacturing equipment.
14. The system of claim 8 , wherein the corrective action comprises adjusting one or more variable capacitors associated with a matching network of the manufacturing equipment based on the predictive data.
15. A non-transitory machine-readable storage medium storing instructions which, when executed cause a processing device to perform operations comprising:
causing manufacturing equipment to generate a RF signal to energize a processing chamber associated with the manufacturing equipment;
receiving, from one or more sensors associated with the manufacturing equipment, current trace data associated the RF signal;
updating impedance values of a digital replica associated with the manufacturing equipment based on the current trace data, wherein the digital replica comprises a model reflecting a virtual representation of physical elements and dynamics of how the manufacturing equipment operates;
obtaining, from the digital replica, one or more outputs indicative of predictive data; and
causing, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment.
16. The non-transitory machine-readable storage medium of claim 15 , wherein the operations further comprise:
responsive to determining that digital replica fails to satisfy an accuracy threshold criterion based on the current trace data, performing an optimization of the digital replica.
17. The non-transitory machine-readable storage medium of claim 15 , wherein updating the impedance values of the digital replica comprises:
updating a processing chamber model associated with the digital replica using trace data from an output impedance sensor, wherein the output impedance sensor is associated with a matching network of the manufacturing equipment.
18. The non-transitory machine-readable storage medium of claim 15 , wherein updating the impedance values of the digital replica comprises:
updating a matching network model associated with the digital replica using trace data from an input impedance sensor and an output impedance sensor, wherein the input impedance sensor and the output impedance sensor are associated with a matching network of the manufacturing equipment.
19. The non-transitory machine-readable storage medium of claim 15 , wherein the one or more outputs indicative of predictive data are generated using a trained machine learning model.
20. The non-transitory machine-readable storage medium of claim 15 , wherein the predictive data comprises one or more tune settings for one or more variable capacitors associated with a matching network of the manufacturing equipment, and wherein the corrective action comprises adjusting one or more variable capacitors associated with a matching network of the manufacturing equipment based on the predictive data.